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Molecular Simulation Workflows as Parallel Algorithms: The Execution Engine of Copernicus, a Distributed High-Performance Computing Platform
KTH, School of Engineering Sciences (SCI), Theoretical Physics, Theoretical & Computational Biophysics. KTH, Centres, SeRC - Swedish e-Science Research Centre.
KTH, School of Engineering Sciences (SCI), Theoretical Physics, Theoretical & Computational Biophysics. KTH, Centres, SeRC - Swedish e-Science Research Centre.
KTH, School of Engineering Sciences (SCI), Theoretical Physics, Theoretical & Computational Biophysics. KTH, Centres, SeRC - Swedish e-Science Research Centre.
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2015 (English)In: Journal of Chemical Theory and Computation, ISSN 1549-9618, E-ISSN 1549-9626, Vol. 11, no 6, 2600-2608 p.Article in journal (Refereed) Published
Abstract [en]

Computational chemistry and other simulation fields are critically dependent on computing resources, but few problems scale efficiently to the hundreds of thousands of processors available in current supercomputers particularly for molecular dynamics. This has turned into a bottleneck as new hardware generations primarily provide mote processing units rather than making individual units much faster, which simulation applications are addressing by increasingly focusing on sampling with algorithms such as free-energy perturbation, Markov state modeling, metadynamics, or milestoning. All these rely on combining results from multiple simulations into a single observation. They are potentially powerful approaches that aim to predict experimental observables directly, but this comes at the expense of added complexity in selecting sampling strategies and keeping track of dozens to thousands of simulations and their dependencies. Here, we describe how the distributed execution framework Copernicus allows the expression of such algorithms in generic workflows: dataflow programs. Because dataflow algorithms explicitly state dependencies of each constituent part, algorithms only need to be described on conceptual level, after which the execution is maximally parallel. The fully automated execution facilitates the optimization of these algorithms with adaptive sampling, where undersampled regions are automatically detected and targeted without user intervention. We show how several such algorithms can be formulated for computational chemistry problems, and how they are executed efficiently with many loosely coupled simulations using either distributed or parallel resources with Copernicus.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2015. Vol. 11, no 6, 2600-2608 p.
National Category
Physical Chemistry
Identifiers
URN: urn:nbn:se:kth:diva-170691DOI: 10.1021/acs.jctc.5b00234ISI: 000356201700020Scopus ID: 2-s2.0-84931287882OAI: oai:DiVA.org:kth-170691DiVA: diva2:840228
Funder
EU, European Research Council, 209825Swedish Research Council, 2013-5901
Note

QC 20150707

Available from: 2015-07-07 Created: 2015-07-03 Last updated: 2017-12-04Bibliographically approved

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Lindahl, Erik

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